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Okada JI, Washio T, Sugiura S, Hisada T. Transition mechanisms from atrial flutter to atrial fibrillation during anti-tachycardia pacing therapy. Pacing Clin Electrophysiol 2023; 46:1509-1518. [PMID: 37922381 DOI: 10.1111/pace.14863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/05/2023]
Abstract
BACKGROUND Atrial anti-tachycardia pacing (aATP) has been shown to be effective for the termination of atrial tachyarrhythmias, but its success rate is still not high enough. OBJECTIVE The main objective of this study was to investigate the mechanisms of atrial flutter (AFL) termination by aATP and the transition from AFL to atrial fibrillation (AF) during aATP. METHODS We developed a multi-scale model of the human atrium based on magnetic resonance images and examined the atrial electrophysiology of AFL during aATP with a ramp protocol. RESULTS In successful cases of aATP, paced excitation entered the excitable gap and collided with the leading edge of the reentrant wave front. Furthermore, the excitation propagating in the opposite direction collided with the trailing edge of the reentrant wave to terminate AFL. The second collision was made possible by the distribution of the wave propagation velocity in the atria. The detailed analysis revealed that the slowing of propagation velocity occurred at the exit of the sub-Eustachian isthmus, probably due to source-sink mismatch. During the transition from AFL to AF, the excitation collided with the refractory zone of the preceding wave and broke into multiple wave fronts to induce AF. A similar observation was made for the transition from AF to sinus rhythm. In both cases, the complex anatomy of the atria played an essential role. CONCLUSION The complex anatomy of atria plays an essential role in the maintenance of stable AFL and its termination by aATP, which were revealed by the realistic three-dimensional simulation model.
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Affiliation(s)
- Jun-Ichi Okada
- UT-Heart Inc., Setagaya-ku, Tokyo, Japan
- Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan
| | - Takumi Washio
- UT-Heart Inc., Setagaya-ku, Tokyo, Japan
- Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan
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Chen S, Jiang L, Xu F, Pang J, Pan C, Chen Y, Wang J, Li K. Electrical-mechanical dynamical coupling between electrocardiographic and photoplethysmographic signals during cardiopulmonary resuscitation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107809. [PMID: 37757567 DOI: 10.1016/j.cmpb.2023.107809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrest (CA) remains a significant cause of death and disability. High-quality cardiopulmonary resuscitation (CPR) can improve the survival rate of CA. A challenging issue is to find physiological indicators for screening and evaluating the cardiovascular function associated with CPR. This study aimed to investigate the electrical-mechanical dynamic coupling between electrocardiographic (ECG) and photoplethysmographic (PPG) signals for indicating cardiovascular function in the progress of CPR. METHOD The ECG and PPG signals were simultaneously collected from a porcine CA model (n = 10) induced by ventricular fibrillation, and were further divided into four periods: Baseline, CA, CPR, and recovery of spontaneous circulation (ROSC). Recurrence quantitative analysis (RQA) was applied to examine the nonlinear dynamics of the ECG and PPG signals individually, and cross recurrence quantitative analysis (CRQA) was used to examine the ECG-PPG dynamical coupling. RESULTS The CA influenced the dynamic patterns of electrical and mechanical activities and the electrical-mechanical coupling, which can be observed from the reduced entropy (ENTR) (p < 0.01), reduced determinism (DET) (p < 0.01) and reduced trapping time (TT) (p < 0.01) at CA compared to Baseline. The recurrence rate (RR), ENTR, DET, and TT at CPR were significantly lower than the parameters at ROSC but higher than those at CA. CONCLUSIONS The electrical-mechanical dynamical coupling was sensitive to CPR and able to reflect the changes in cardiac function in the process of CPR.
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Affiliation(s)
- Shuxin Chen
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lijun Jiang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Xu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Jiaojiao Pang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Chang Pan
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Yuguo Chen
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Jiali Wang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Ke Li
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
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Pilia N, Schuler S, Rees M, Moik G, Potyagaylo D, Dössel O, Loewe A. Non-invasive localization of the ventricular excitation origin without patient-specific geometries using deep learning. Artif Intell Med 2023; 143:102619. [PMID: 37673581 DOI: 10.1016/j.artmed.2023.102619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 06/18/2023] [Accepted: 06/24/2023] [Indexed: 09/08/2023]
Abstract
Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This type of arrhythmia can be caused by focal activation sources outside the sinus node. Catheter ablation of these foci is a curative treatment in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) that address these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Moreover, one of the method's outputs can be interpreted as several ranked solutions. The CNNs were trained on a dataset containing only simulated data and evaluated both on simulated test data and clinical data. On a novel large and open simulated dataset, the median test error was below 3 mm. The median localization error on the unseen clinical data ranged from 32 mm to 41 mm without optimizing the pre-processing and CNN to the clinical data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions decreased to 20 mm on the clinical data. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need for patient-specific geometrical information. Furthermore, providing multiple solutions can assist physicians in identifying the true activation source amongst more than one possible location. With further optimization to clinical data, these methods have high potential to accelerate clinical interventions, replace certain steps within these procedures and consequently reduce procedural risk and improve VT patient outcomes.
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Affiliation(s)
- Nicolas Pilia
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
| | - Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Maike Rees
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gerald Moik
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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4
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Vila M, Rivolta MW, Barrios Espinosa CA, Unger LA, Luik A, Loewe A, Sassi R. Recommender system for ablation lines to treat complex atrial tachycardia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107406. [PMID: 36787660 DOI: 10.1016/j.cmpb.2023.107406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.
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Affiliation(s)
- Muhamed Vila
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
| | - Massimo W Rivolta
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy.
| | - Cristian A Barrios Espinosa
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Laura A Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestraße 90, Karlsruhe, 76133, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Germany
| | - Roberto Sassi
- Università degli Studi di Milano, Via Celoria 18, Milan, 20133, Italy
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5
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The nonlinearity properties of pulse signal of pregnancy in the three trimesters. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Luongo G, Vacanti G, Nitzke V, Nairn D, Nagel C, Kabiri D, Almeida TP, Soriano DC, Rivolta MW, Ng GA, Dössel O, Luik A, Sassi R, Schmitt C, Loewe A. Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram. Europace 2022; 24:1186-1194. [PMID: 35045172 PMCID: PMC9301972 DOI: 10.1093/europace/euab322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/12/2021] [Indexed: 11/12/2022] Open
Abstract
Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.
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Affiliation(s)
- Giorgio Luongo
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Gaetano Vacanti
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestrasse, 90, 76182, Karlsruhe, Germany
| | - Vincent Nitzke
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Diba Kabiri
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestrasse, 90, 76182, Karlsruhe, Germany
| | - Tiago P Almeida
- Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Diogo C Soriano
- Engineering, Modelling and Applied Social Sciences Centre, ABC Federal University, São Bernardo do Campo, Brazil
| | - Massimo W Rivolta
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Ghulam André Ng
- Department of Cardiovascular Sciences, University of Leicester, NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Olaf Dössel
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestrasse, 90, 76182, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Claus Schmitt
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Moltkestrasse, 90, 76182, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
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Vila M, Rivolta MW, Luongo G, Unger LA, Luik A, Gigli L, Lombardi F, Loewe A, Sassi R. Atrial Flutter Mechanism Detection Using Directed Network Mapping. Front Physiol 2021; 12:749635. [PMID: 34764882 PMCID: PMC8577834 DOI: 10.3389/fphys.2021.749635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.
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Affiliation(s)
- Muhamed Vila
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | | | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Anna Unger
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Lorenzo Gigli
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Federico Lombardi
- UOC Malattie Cardiovascolari, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
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Abstract
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
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Sánchez J, Luongo G, Nothstein M, Unger LA, Saiz J, Trenor B, Luik A, Dössel O, Loewe A. Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset. Front Physiol 2021; 12:699291. [PMID: 34290623 PMCID: PMC8287829 DOI: 10.3389/fphys.2021.699291] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 11/15/2022] Open
Abstract
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Laura A. Unger
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
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10
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Luongo G, Azzolin L, Schuler S, Rivolta MW, Almeida TP, Martínez JP, Soriano DC, Luik A, Müller-Edenborn B, Jadidi A, Dössel O, Sassi R, Laguna P, Loewe A. Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:126-136. [PMID: 33899043 PMCID: PMC8053175 DOI: 10.1016/j.cvdhj.2021.03.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.
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Affiliation(s)
- Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Luca Azzolin
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Massimo W. Rivolta
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Tiago P. Almeida
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | | | - Diogo C. Soriano
- Engineering, Modelling and Applied Social Sciences Centre, ABC Federal University, São Bernardo do Campo, Brazil
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Björn Müller-Edenborn
- Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany
| | - Amir Jadidi
- Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy
| | - Pablo Laguna
- I3A, Universidad de Zaragoza, and CIBER-BNN, Zaragoza, Spain
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Luongo G, Azzolin L, Rivolta MW, Sassi R, Martinez JP, Laguna P, Dossel O, Loewe A. Non-Invasive Identification of Atrial Fibrillation Driver Location Using the 12-lead ECG: Pulmonary Vein Rotors vs. other Locations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:410-413. [PMID: 33018015 DOI: 10.1109/embc44109.2020.9176135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.
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